A. Field of the Invention
This invention pertains to decision analysis, and more particularly to decision analysis software employing Bayesian networks (“BN”), also known as “belief networks”.
B. Background
Decision systems are of increasing importance in today's society as the volume of available information explodes and the required computing capability for analyzing this information vastly increases.
One type of decision system is a rule-based system (“RBS”). This type of system has several disadvantages. While the user may view individual rules, the system is otherwise a “black box”. In addition, performance degrades proportionally to the size of the database. Further, a RBS may produce undesirable results if the processing is “noisy” or if the information is incomplete. Even further, the information per se is exceedingly difficult if not altogether impossible to separate from the software embodying the knowledge.
In some cases, a RBS can create contradictory answers. RBS code typically analyzes a first section of the data and creates a multitude of rules that predict a value. Later, additional data is available, and additional rules are created to handle the newly learned areas. What can now exist is a case where, with a given set of data, the answer is positive, but when taken in a different order, the answer is negative, both with the same data. This can obviously lead to unacceptable results.
Another type of decision system employs a neural network (“NN”). NNs also have disadvantages. For example, they are primarily also “black boxes”, allowing virtually no observation or understanding of the results of their systems or reasoning. Like RBS, their knowledge cannot be separated from their software implementation. Further, the non-linear approach of a NN produces results that can vary for identical inputs.
Various other attempts have been made to advance decision systems. For example, U.S. Pat. No. 5,715,374 discloses a method and system for case-based reasoning (“CBR”) employing a belief network. This patent, however, fails to show at least employment of a pre-existing database or intelligent decision analysis. U.S. Pat. No. 5,704,017 discloses an improved collaborative filtering system using a belief network. It suffers from many of the same deficiencies as the '374 patent above. U.S. Pat. No. 5,987,415 discloses a technique employing a BN that uses inputs from a user to model the user's emotion and personality, but is deficient as an integrated decision engine for at least similar reasons as the patents above. U.S. Pat. No. 5,704,018 and its related cases disclose a belief network in which expert knowledge and empirical data form the basis for creation of the network; U.S. Pat. No. 6,056,690 discloses a belief network for diagnosing breast cancer; U.S. Pat. No. 6,024,705 discloses Bayesian analysis to partially analyze heart performance and diagnose myocardial ischemia; all of these references disclose BNs but do not employ at least some techniques characteristic of intelligent decision systems.